Luma Uni-1.1vs.Stable Diffusion Turbo
Which one is better?
In our own test, Luma Uni-1.1 leads. Neither is in the arena. Neither has an API price.
Test images
Same task, the first image of each. The task with the biggest difference is preselected.
Result for “Infographic”:Luma Uni-1.1 ahead
What the rating looks at: Numbers, labels, and visual organization.
“Ahead”: better in all three rating rounds. “Slightly ahead”: a higher value, but not in every round.
Test protocol of the images
Each test attempt uses the same archived prompt and one request. The first output of that attempt counts. Repeats after technical test failures are disclosed for the affected images. Files are normalized to 1,024 × 1,024 pixels without cropping. Provider resolution, randomness, quality tier and prompt processing can still differ. The images are a practical visual test, not a reconstruction of the arena configuration.
Character and detail
Luma Uni-1.1: fal, aspect ratio 1:1, n=1, seed=provider random, Fal schema does not expose an output-resolution parameter; require square output of at least 1024px, estimated cost $0.042000, source 2048x2048, stored 1024x1024 (Lanczos3 downscale), generated Nov 1, 2026, 8:49 PM. Source
Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=5483236571890770311; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE; native tokenizer truncation: tokenizer 80 to 77 tokens; archived input unchanged, generated Nov 2, 2026, 6:17 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 985a75b38642a9f0e5e0c0e0c67f1b7f67780d42bd2168fa24e9a7a7a94b0da6
Luma Uni-1.1 image SHA 256: 4210462a5210fcde39e1f78167cb160cd3966176012d0ef2c187c6490d6a47e4
Stable Diffusion Turbo image SHA 256: 291b69ba3d375bfa017c782f61521b705d67708ed5e1be9350d55d486ad6f1d1
Infographic
Luma Uni-1.1: fal, aspect ratio 1:1, n=1, seed=provider random, Fal schema does not expose an output-resolution parameter; require square output of at least 1024px, estimated cost $0.042000, source 2048x2048, stored 1024x1024 (Lanczos3 downscale), generated Nov 1, 2026, 8:50 PM. Source
Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=8161577731918994504; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:18 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: ed65cb4e6a51e2336f8b149ad4cbb16e020f07d527ad94362e719d2dcf91d94f
Luma Uni-1.1 image SHA 256: fef94f2000cb22c90b2a147753ace395ee6c14f3e803e0cc88376556dd5267bc
Stable Diffusion Turbo image SHA 256: 80a2687dbc408395b416994d4853332427d09fccead1a6229564cc25b32de7e6
Product photography
Luma Uni-1.1: fal, aspect ratio 1:1, n=1, seed=provider random, Fal schema does not expose an output-resolution parameter; require square output of at least 1024px, estimated cost $0.042000, source 2048x2048, stored 1024x1024 (Lanczos3 downscale), generated Nov 1, 2026, 8:47 PM. Source
Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=2124044582169182899; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE; native tokenizer truncation: tokenizer 80 to 77 tokens; archived input unchanged, generated Nov 2, 2026, 6:16 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: c67fd71e91304458a2feef5c91efa8677260fab0c0099a29a2c52cb579f5b2f3
Luma Uni-1.1 image SHA 256: 9e31a8397f6dd1f0a09a0440510edcdbfbac5470f75a78ec6c6b67783099e5c2
Stable Diffusion Turbo image SHA 256: 13dcbe6eb77495317a03f3eed013c4358ae7466fca00d9f869e512279a8a2af6
Typography and layout
Luma Uni-1.1: fal, aspect ratio 1:1, n=1, seed=provider random, Fal schema does not expose an output-resolution parameter; require square output of at least 1024px, estimated cost $0.042000, source 2048x2048, stored 1024x1024 (Lanczos3 downscale), generated Nov 1, 2026, 8:46 PM. Source
Stable Diffusion Turbo: manual, local PyTorch 2.13.0, Diffusers 0.36.0; stabilityai/sd-turbo@b261bac6fd2cf515557d5d0707481eafa0485ec2; native 1024x1024, 1:1; one request, first output; steps=1, guidance=0.0, seed=2303175353176269810; mps for text encoder and denoising; cpu for native VAE decode; float16 for text encoder and transformer; float32 for VAE, generated Nov 2, 2026, 6:16 PM. Source
Protocol gradually-image-comparison-v1. Prompt SHA 256: 2e012c3d14ee5dd02203a94f394ca80a5038cdebd6032b25a8b8a221f2255419
Luma Uni-1.1 image SHA 256: 97710d52e2207b2bf7db070cf1dfa54af5ce79fd7a1fec12bdb1616c648c01f4
Stable Diffusion Turbo image SHA 256: 7def19e4f4adf68244ff69e5873b9f465ecd158fb4abab0273443bfc3804438e
The numbers at a glance
Luma Uni-1.1
not in the arena
Stable Diffusion Turbo
not in the arena
Luma Uni-1.1
Rank 33 of 108
Stable Diffusion Turbo
Rank 108 of 108
Luma Uni-1.1
no API price
Stable Diffusion Turbo
no API price
Ranks among the 21 image models with an arena rating and the 108 with a GraduallyBench run; prices compared with 28 models with an API price.
Luma Uni-1.1
- Subscription
- No Luma AI subscription includes this model. Through other providers from €19.04 a month (Magnific Premium, with limited credits). 6 plans in Germany.
- Via API
- No API price known. Access: API.
- Weights
- unknown.
Stable Diffusion Turbo
- Subscription
- n/a No plan in Germany names exactly this model.
- Via API
- No API price known. Access: local.
- License
- Open weights.
Subscriptions only count plans that name exactly this model. Subscription prices include VAT, API prices are in dollars.
GraduallyBench from Nov 3, 2026 · Subscription database from Sep 29, 2026 (38 days old)
All data side by side
The better value is bold. For ratings only when the gap is clear.
| Kennzahl | Luma Uni-1.1 | Stable Diffusion Turbo |
|---|---|---|
| Ratings | ||
| Arena ratingfrom votes by users, higher is better | n/a | n/a |
| Arena rank | n/a | n/a |
| Arena votesmore votes, a safer rating | n/a | n/a |
| GraduallyBenchour own rating, 0 to 100 | 66 | 3 |
| GraduallyBench by task | ||
| Typography and layoutLegible text, hierarchy, and composition | 67 | 0 |
| Product photographyMaterials, reflections, and fine details | 60 | 1 |
| Character and detailAnatomy, clothing, and spatial consistency | 49 | 5 |
| InfographicNumbers, labels, and visual organization | 89 | 5 |
| API price | ||
| Per imagelower is better | n/a | n/a |
| 100 images | n/a | n/a |
| Profile | ||
| Released | May 11, 2026 | n/a |
| Weightsopen means: runs locally | unknown | open |
| Lifecycle | no notice | Older open fast checkpoint; no active hosted API ID claimed in the reviewed model list |
| Access | API | local |
| Parameters | not published | not published |
| Largest output | not consistently published | not consistently published |
| Plans in Germanynaming exactly this model | 6 | n/a |
API prices for one image at 1,024 × 1,024 pixels according to Artificial Analysis.
Sources: Luma Uni-1.1 model source (verified Nov 1, 2026) · Stable Diffusion Turbo model source, status source (verified Nov 1, 2026)
Arena ratings by category
From blind votes on LMArena. “Ahead” means the lead is clear.
No shared arena score in the stored snapshot
The stored benchmark database contains no measurement for Luma Uni-1.1 and Stable Diffusion Turbo from the same arena and category. Older model versions are not used as substitutes.
More comparisons
Compare Luma Uni-1.1 and Stable Diffusion Turbo with the models people compare them with most often.